论文标题
多污染图融合用于病理学多模式整合
Multi stain graph fusion for multimodal integration in pathology
论文作者
论文摘要
在病理学中，使用多种染色技术评估组织样品，以增强独特的组织学特征的对比度。在本文中，我们介绍了一种基于多模式CNN-GNN的图形融合方法，该方法利用了来自多个未注册的组织病理学图像的互补信息来预测病理分数。我们通过预测CRN纤维化阶段和NAFLD活性评分（NAS）来证明这种方法在非酒精性脂肪性肝炎（NASH）中。对NASH的主要评估通常需要对两种组织学染色的肝活检评估：三色（TC）以及苏木精和曙红（H＆E）。我们的多模式方法学会从与每个污渍相对应的TC和H＆E图中提取互补信息，同时学习结合此信息的最佳策略。我们报告在预测纤维化阶段和NAS成分等级上比单染色模型方法提高了20％，该方法通过计算在机器衍生的与病理学家共识分数之间计算线性加权的Cohen的Kappa来衡量。从广义上讲，本文展示了利用多种病理图像以改善ML供电的组织学评估的价值。
In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machine-derived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.
